Notice number: NOT-OD-09-058 Notice Title: NIH Announces the Availability of Recovery Act funds for Competitive Revision Application ABSTRACT At the level of an individual, aging is a process shaped by multiple stochastic, genetic, and environmental factors, all of which conspire to set a range of possible death times. At the level of a population, it is the endpoint of the aging process-death-that comes into focus. A large number of individuals sample the probability distribution of death times, giving rise to a survival curve that constitutes the demographic signature of aging. The survival curve is a central piece of statistical data useful to researchers regardless of which aspects of aging they study at what level of granularity. The significance of the full survival curve as an assay in C. elegans aging research can be greatly enhanced by expanding its statistical and temporal resolution beyond the practical limitations set by manual protocols. For this purpose we have developed an automated method for acquiring survival curves in C. elegans populations by adapting flatbed scanners for imaging Petri plates containing worms and automatically converting the resultant time series of images into a survival curve. High-resolution survival curves would be an ideal way to classify genes in terms of subtle impacts on curve shape. Moreover, the standardization inherent in an automated method makes survival data acquired for the same genetic (or environmental) perturbation cumulative across laboratories. Finally, automatically acquired curves can be compared across laboratories as well as against all other curves previously acquired by the same method, creating a data "network effect". Anticipating outside interest in our procedure, our proposal aims to adapt our technology for successful transfer to other laboratories. PUBLIC HEALTH RELEVANCE: The survival curve of genetically identical individuals is a powerful tool in the study of aging. A robust technology for the automated and inexpensive generation of high-resolution survival curves of large worm (C. elegans) populations will have broad impact on the study of aging. We have developed a prototype of such a technology and will adapt it for successful transfer to other laboratories.